Should Machine Learning Models Report to Us When They Are Clueless?
This work tackles the issue of AI transparency for policymakers and stakeholders, though it is incremental in proposing regulatory solutions rather than novel technical methods.
The paper addresses the problem of AI models extrapolating beyond their training data without notifying users, highlighting a missing component in explainability. It proposes practical regulatory clauses to enhance transparency and accountability in AI systems.
The right to AI explainability has consolidated as a consensus in the research community and policy-making. However, a key component of explainability has been missing: extrapolation, which describes the extent to which AI models can be clueless when they encounter unfamiliar samples (i.e., samples outside the convex hull of their training sets, as we will explain). We report that AI models extrapolate outside their range of familiar data, frequently and without notifying the users and stakeholders. Knowing whether a model has extrapolated or not is a fundamental insight that should be included in explaining AI models in favor of transparency and accountability. Instead of dwelling on the negatives, we offer ways to clear the roadblocks in promoting AI transparency. Our analysis commentary accompanying practical clauses useful to include in AI regulations such as the National AI Initiative Act in the US and the AI Act by the European Commission.